Integrating Transcriptomic Data with Mechanistic Systems Pharmacology Models for Virtual Drug Combination Trials

SPOPL induces tumorigenicity and stemness in glioma stem cells by activating Notch signaling

OBJECTIVE

Recent studies have increasingly shown that glioma stem cells (GSCs) are extremely important for developing and treating glioblastoma multiforme (GBM). The Broad-complex, Tram-track, and Bric-a-brac protein family is functionally related to a variety of tumor stem cells, and the role of SPOPL as a member of this family in GSCs deserves to be investigated.

METHODS

To investigate the expression of SPOPL in GSCs and its impact on the prognosis of GBM patients by using clinical specimens, patient-derived primary GSCs and public databases. In vivo and in vitro, the effect of SPOPL on the proliferation, self-renewal, and differentiation ability of GSCs was explored. Probing the mechanism by which SPOPL affects the biological function of GSCs using RNA sequencing (RNA-seq) and rescue experiments.

RESULTS

The expression of SPOPL was significantly upregulated in GSCs and GBM, and patients with high SPOPL expression had a poorer prognosis. SPOPL enhanced the proliferation and self-renewal ability of GSCs and enhanced the tumorigenicity of GSCs. The Notch signaling pathway was significantly inhibited in SPOPL knockdown GSCs. Activation or inhibition of the Notch signaling pathway rescued changes in the biological function of GSCs caused by altered SPOPL expression.

CONCLUSION

SPOPL can be used as a potential prognostic biomarker for GBM in clinical work and promotes the proliferation and stemness of GSCs by activating the Notch signaling pathway, which may be a potential molecule for targeting GSCs to treat GBM.

Computational speed-up of large-scale, single-cell model simulations via a fully integrated SBML-based format

Summary

Large-scale and whole-cell modeling has multiple challenges, including scalable model building and module communication bottlenecks (e.g. between metabolism, gene expression, signaling, etc.). We previously developed an open-source, scalable format for a large-scale mechanistic model of proliferation and death signaling dynamics, but communication bottlenecks between gene expression and protein biochemistry modules remained. Here, we developed two solutions to communication bottlenecks that speed-up simulation by ∼4-fold for hybrid stochastic-deterministic simulations and by over 100-fold for fully deterministic simulations. Fully deterministic speed-up facilitates model initialization, parameter estimation and sensitivity analysis tasks.

Availability and implementation

Source code is freely available at https://github.com/birtwistlelab/SPARCED/releases/tag/v1.3.0 implemented in python, and supported on Linux, Windows and MacOS (via Docker).

Scott W, Sheriff RSM, Suarez Diez M, Van Steen K, Westerhoff HV, Wittig U, Wolstencroft K, Zupanic A, Evelo CT, Hancock JM. Systems Biology in ELIXIR: modelling in the spotlight

In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR's future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.

Scott W, Sheriff RSM, Suarez Diez M, Van Steen K, Westerhoff HV, Wittig U, Wolstencroft K, Zupanic A, Evelo CT, Hancock JM. Systems Biology in ELIXIR: modelling in the spotlight

In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR's future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.

A scalable, open-source implementation of a large-scale mechanistic model for single cell proliferation and death signaling

Mechanistic models of how single cells respond to different perturbations can help integrate disparate big data sets or predict response to varied drug combinations. However, the construction and simulation of such models have proved challenging. Here, we developed a python-based model creation and simulation pipeline that converts a few structured text files into an SBML standard and is high-performance- and cloud-computing ready. We applied this pipeline to our large-scale, mechanistic pan-cancer signaling model (named SPARCED) and demonstrate it by adding an IFNγ pathway submodel. We then investigated whether a putative crosstalk mechanism could be consistent with experimental observations from the LINCS MCF10A Data Cube that IFNγ acts as an anti-proliferative factor. The analyses suggested this observation can be explained by IFNγ-induced SOCS1 sequestering activated EGF receptors. This work forms a foundational recipe for increased mechanistic model-based data integration on a single-cell level, an important building block for clinically-predictive mechanistic models.

Deciphering the signaling network of breast cancer improves drug sensitivity prediction

One goal of precision medicine is to tailor effective treatments to patients' specific molecular markers of disease. Here, we used mass cytometry to characterize the single-cell signaling landscapes of 62 breast cancer cell lines and five lines from healthy tissue. We quantified 34 markers in each cell line upon stimulation by the growth factor EGF in the presence or absence of five kinase inhibitors. These data-on more than 80 million single cells from 4,000 conditions-were used to fit mechanistic signaling network models that provide insight into how cancer cells process information. Our dynamic single-cell-based models accurately predicted drug sensitivity and identified genomic features associated with drug sensitivity, including a missense mutation in DDIT3 predictive of PI3K-inhibition sensitivity. We observed similar trends in genotype-drug sensitivity associations in patient-derived xenograft mouse models. This work provides proof of principle that patient-specific single-cell measurements and modeling could inform effective precision medicine strategies.

"Zooming in" on Glioblastoma: Understanding Tumor Heterogeneity and its Clinical Implications in the Era of Single-Cell Ribonucleic Acid Sequencing

Glioblastoma (GBM) is the most common primary brain malignancy in adults and one of the most aggressive of all human cancers. It is highly recurrent and treatment-resistant, in large part due to its infiltrative nature and inter- and intratumoral heterogeneity. This heterogeneity entails varying genomic landscapes and cell types within and between tumors and the tumor microenvironment (TME). In GBM, heterogeneity is a driver of treatment resistance, recurrence, and poor prognosis, representing a substantial impediment to personalized medicine. Over the last decade, sequencing technologies have facilitated deeper understanding of GBM heterogeneity by "zooming in" progressively further on tumor genomics and transcriptomics. Initial efforts employed bulk ribonucleic acid (RNA) sequencing, which examines composite gene expression of whole tumor specimens. While groundbreaking at the time, this bulk RNAseq masks the crucial contributions of distinct tumor subpopulations to overall gene expression. This work progressed to the use of bulk RNA sequencing in anatomically and spatially distinct tumor subsections, which demonstrated previously underappreciated genomic complexity of GBM. A revolutionary next step forward has been the advent of single-cell RNA sequencing (scRNAseq), which examines gene expression at the single-cell level. scRNAseq has enabled us to understand GBM heterogeneity in unprecedented detail. We review seminal studies in our progression of understanding GBM heterogeneity, with a focus on scRNAseq and the insights that it has provided into understanding the GBM tumor mass, peritumoral space, and TME. We highlight preclinical and clinical implications of this work and consider its potential to impact neuro-oncology and to improve patient outcomes via personalized medicine.

In vitro modeling of the neurovascular unit: advances in the field

The blood–brain barrier (BBB) is a fundamental component of the central nervous system. Its functional and structural integrity is vital in maintaining the homeostasis of the brain microenvironment. On the other hand, the BBB is also a major hindering obstacle for the delivery of effective therapies to treat disorders of the Central Nervous System (CNS). Over time, various model systems have been established to simulate the complexities of the BBB. The development of realistic in vitro BBB models that accurately mimic the physiological characteristics of the brain microcapillaries in situ is of fundamental importance not only in CNS drug discovery but also in translational research. Successful modeling of the Neurovascular Unit (NVU) would provide an invaluable tool that would aid in dissecting out the pathological factors, mechanisms of action, and corresponding targets prodromal to the onset of CNS disorders. The field of BBB in vitro modeling has seen many fundamental changes in the last few years with the introduction of novel tools and methods to improve existing models and enable new ones. The development of CNS organoids, organ-on-chip, spheroids, 3D printed microfluidics, and other innovative technologies have the potential to advance the field of BBB and NVU modeling. Therefore, in this review, summarize the advances and progress in the design and application of functional in vitro BBB platforms with a focus on rapidly advancing technologies.

Personalized signaling models for personalized treatments

Dynamic mechanistic models, that is, those that can simulate behavior over time courses, are a cornerstone of molecular systems biology. They are being used to model molecular mechanisms with varying degrees of granularity-from elementary reactions to causal links-and to describe these systems by various dynamic mathematical frameworks, such as Boolean networks or systems of differential equations. The models can be based exclusively on experimental data, or on prior knowledge of the underlying biological processes. The latter are typically generic, but can be adapted to a certain context, such as a particular cell type, after training with context-specific data. Dynamic mechanistic models that are based on biological knowledge have great potential for modeling specific systems, because they require less data for training to provide biological insight in particular into causal mechanisms, and to extrapolate to scenarios that are outside the conditions they have been trained on.

Umbilical cord-derived mesenchymal stromal/stem cells expressing IL-24 induce apoptosis in gliomas

Although much progress has been made in the treatment of gliomas, the prognosis for patients with gliomas is still very poor. Stem cell-based therapies may be promising options for glioma treatment. Recently, many studies have reported that umbilical cord-derived mesenchymal stromal/stem cells (UC-MSCs) are ideal gene vehicles for tumor gene therapy. Interleukin 24 (IL-24) is a pleiotropic immunoregulatory cytokine that has an apoptotic effect on many kinds of tumor cells and can inhibit the growth of tumors specifically without damaging normal cells. In this study, we investigated UC-MSCs as a vehicle for the targeted delivery of IL-24 to tumor sites. UC-MSCs were transduced with lentiviral vectors carrying green fluorescent protein (GFP) or IL-24 complementary DNA. The results indicated that UC-MSCs could selectively migrate to glioma cells in vitro and in vivo. Injection of IL-24-UC-MSCs significantly suppressed tumor growth of glioma xenografts. The restrictive efficacy of IL-24-UC-MSCs was associated with the inhibition of proliferation as well as the induction of apoptosis in tumor cells. These findings indicate that UC-MSC-based IL-24 gene therapy may be able to suppress the growth of glioma xenografts, thereby suggesting possible future therapeutic use in the treatment of gliomas.

Model systems for studying the blood-brain barrier: Applications and challenges

The blood-brain barrier (BBB) poses a serious impediment to the delivery of effective therapies to the central nervous system (CNS). Over time, various model systems have been crafted and used to evaluate the complexities of the BBB, which includes an impermeable physical barrier and a series of energy-dependent efflux pumps. Models of the BBB have mainly sought to assess changes in endothelial cell permeability, the role of ATP-dependent efflux transporters in drug disposition, and alterations in communication between BBB cells and the microenvironment. In the context of disease, various animal models have been utilized to examine real time BBB drug permeability, CNS dynamic changes, and overall treatment response. In this review, we outline the use of these in vitro and in vivo blood-brain barrier model systems to study normal physiology and diseased states. These current models each have their own advantages and disadvantages for studying the response of biologic processes to physiological and pathological conditions. Additional models are needed to mimic more closely the dynamic quality of the BBB, with the goal focused on potential clinical applications.

Systems Pharmacology: Defining the Interactions of Drug Combinations

The majority of diseases are associated with alterations in multiple molecular pathways and complex interactions at the cellular and organ levels. Single-target monotherapies therefore have intrinsic limitations with respect to their maximum therapeutic benefits. The potential of combination drug therapies has received interest for the treatment of many diseases and is well established in some areas, such as oncology. Combination drug treatments may allow us to identify synergistic drug effects, reduce adverse drug reactions, and address variability in disease characteristics between patients. Identification of combination therapies remains challenging. We discuss current state-of-the-art systems pharmacology approaches to enable rational identification of combination therapies. These approaches, which include characterization of mechanisms of disease and drug action at a systems level, can enable understanding of drug interactions at the molecular, cellular, physiological, and organismal levels. Such multiscale understanding can enable precision medicine by promoting the rational development of combination therapy at the level of individual patients for many diseases.

Spatiotemporal switching signals for cancer stem cell activation in pediatric origins of adulthood cancer: Towards a watch-and-wait lifetime strategy for cancer treatment

Pediatric origin of cancer stem cell hypothesis holds great promise and potential in adult cancer treatment, however; the road to innovation is full of obstacles as there are plenty of questions left unanswered. First, the key question is to characterize the nature of such stem cells (concept). Second, the quantitative imaging of pediatric stem cells should be implemented (technology). Conceptually, pediatric stem cell origins of adult cancer are based on the notion that plasticity in early life developmental programming evolves local environments to cancer. Technologically, such imaging in children is lacking as all imaging is designed for adult patients. We postulate that the need for quantitative imaging to measure space-time changes of plasticity in early life developmental programming in children may trigger research and development of the imaging technology. Such quantitative imaging of pediatric origin of adulthood cancer will help develop a spatiotemporal monitoring system to determine cancer initiation and progression. Clinical validation of such speculative hypothesis-that cancer originates in a pediatric environment-will help implement a wait-and-watch strategy for cancer treatment.

Analysis of copy number loss of the ErbB4 receptor tyrosine kinase in glioblastoma

Current treatments for glioblastoma multiforme (GBM)-an aggressive form of brain cancer-are minimally effective and yield a median survival of 14.6 months and a two-year survival rate of 30%. Given the severity of GBM and the limitations of its treatment, there is a need for the discovery of novel drug targets for GBM and more personalized treatment approaches based on the characteristics of an individual's tumor. Most receptor tyrosine kinases-such as EGFR-act as oncogenes, but publicly available data from the Cancer Cell Line Encyclopedia (CCLE) indicates copy number loss in the ERBB4 RTK gene across dozens of GBM cell lines, suggesting a potential tumor suppressor role. This loss is mutually exclusive with loss of its cognate ligand NRG1 in CCLE as well, more strongly suggesting a functional role. The availability of higher resolution copy number data from clinical GBM patients in The Cancer Genome Atlas (TCGA) revealed that a region in Intron 1 of the ERBB4 gene was deleted in 69.1% of tumor samples harboring ERBB4 copy number loss; however, it was also found to be deleted in the matched normal tissue samples from these GBM patients (n = 81). Using the DECIPHER Genome Browser, we also discovered that this mutation occurs at approximately the same frequency in the general population as it does in the disease population. We conclude from these results that this loss in Intron 1 of the ERBB4 gene is neither a de novo driver mutation nor a predisposing factor to GBM, despite the indications from CCLE. A biological role of this significantly occurring genetic alteration is still unknown. While this is a negative result, the broader conclusion is that while copy number data from large cell line-based data repositories may yield compelling hypotheses, careful follow up with higher resolution copy number assays, patient data, and general population analyses are essential to codify initial hypotheses prior to investing experimental resources.